It's been just over 21 years since one of the darkest days in NASA's history. On the morning of February 1, 2003, Space Shuttle Columbia disintegrated as it reentered the atmosphere over Texas and Louisiana. The seven astronauts aboard – David Brown, Rick Husband, Laurel Clark, Kalpana Chawla, Michael Anderson, William McCool and Ilan Ramon – all lost their lives. The tragic event is being retold for a BBC Two documentary series airing from this week on BBC Two, 'The Space Shuttle That Fell to Earth'. MailOnline has revealed a step-by-step graphic showing exactly what went wrong on that fateful morning, which changed NASA forever.

Etech Global Services is hosting an interactive workshop which will explore the importance of Artificial Intelligence (AI) and Quality Analytics in the contact center this January 17, 2017 at Call Center Week Winter Conference & Expo in New Orleans, Louisiana. Etech's President, Matt Rocco, and Executive Vice President of Customer Experience, Jim Iyoob, will lead the workshop and drive discussion through various interactive activities and in depth Q&A sessions. Artificial intelligence and machine learning are becoming part of the economy in ways Etech could only imagine a decade ago. From self-driving cars to robots, the rapid growth of AI creates countless opportunities to increase productivity and economic growth. Artificial Intelligence is not new, but the underlying technologies have reached an all time high.

Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and the corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of the search process. Our goal is to develop an architecture that automatically selects parallel search strategies for optimal performance on a variety of search problems. In this paper we describe one such architecture realized in the Eureka system, which combines the benefits of many different approaches to parallel heuristic search. Through empirical and theoretical analyses we observe that features of the problem space directly affect the choice of optimal parallel search strategy. We then employ machine learning techniques to select the optimal parallel search strategy for a given problem space. When a new search task is input to the system, Eureka uses features describing the search space and the chosen architecture to automatically select the appropriate search strategy. Eureka has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results generated from fifteen puzzle problems, robot arm motion problems, artificial search spaces, and planning problems indicate that Eureka outperforms any of the tested strategies used exclusively for all problem instances and is able to greatly reduce the search time for these applications.